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dc.contributor.advisorDuane S. Boning.en_US
dc.contributor.authorHe, Han, M. Eng Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Mechanical Engineering.en_US
dc.date.accessioned2019-02-05T16:00:49Z
dc.date.available2019-02-05T16:00:49Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/120246
dc.descriptionThesis: M. Eng. in Advanced Manufacturing and Design, Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 68-69).en_US
dc.description.abstractEarly and accurate anomaly detection plays a key role in reducing costs and improving benefits, especially for complicated and time-consuming manufacturing such as semiconductor production. A case study of detecting anomalies from several monitored parameters during one plasma etching process is presented in this thesis. The thesis focuses on optimized ways to build reference cycles, or centroids of univariate parameters, a critical component to determine clustering accuracy and to facilitate process engineers' offline anomaly detections and diagnoses. Three time series centroid building methods are discussed and evaluated in the thesis, arithmetic, the Dynamic Time Warping Barycenter Averaging (DBA), and the soft-DTW-based centroid. As a result, DBA is chosen considering its comprehensive performance of accuracy and calculation time. Optimizations on DBA is further discussed to reduce calculation time. The window constraint, as well as the recalculation method of combining the previous centroid and new datasets, substantially reduce calculation time with slight accuracy loss. Based upon one centroid building method, shape extraction, a novel clustering method, k-shape, is implemented and applied to the plasma etching process. It is found that it achieves great accuracy with substantially shorter calculation time than one mainstream clustering method, k-means.en_US
dc.description.statementofresponsibilityby Han He.en_US
dc.format.extent76 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMechanical Engineering.en_US
dc.titleApplications of reference cycle building and K-shape clustering for anomaly detection in the semiconductor manufacturing processen_US
dc.typeThesisen_US
dc.description.degreeM. Eng. in Advanced Manufacturing and Designen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.identifier.oclc1083130560en_US


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